Deformation Analysis of an Ultra-High Arch Dam under Different Water Level Conditions Based on Optimized Dynamic Panel Clustering

Liu, Yongtao; Zheng, Dongjian; Georgakis, Christos; Kabel, Thomas; Cao, En-Hua; Wu, Xin; Ma, Jiajia

doi:10.3390/app12010481

Cited by 12 publications

(7 citation statements)

References 45 publications

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“…For the five eigenvalues, we need to calculate the weight of each eigenvalue, and then cluster analysis. Liu et al (2022) used panel data clustering theory to construct a spatio-temporal feature model of dam deformation, proposed three indicators to characterize dam deformation, and calculated the objective weight coefficient of clustering indicators based on CRITIC method. Weight calculation includes subjective weight determination method, objective weight determination method and subjective-objective combined weight determination method.…”

Section: Literature Reviewmentioning

confidence: 99%

Driving Factors of Carbon Emissions in G20 Countries: A Weighted Average Structural Decomposition Approach

Xia

2023

Preprint

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As a major part of global development governance, G20 countries ' carbon emissions account for 80 % of global carbon emissions. In order to achieve the "carbon neutrality" goal proposed by the United Nations, it is important to compare and analyze the driving factors of carbon emissions in G20 countries and provide suggestions for their carbon elimination. Based on the carbon emission input-output data of seventeen countries out of G20 in the EORA database, this paper compares the driving factors of carbon emissions between the countries from 1990 to 2016 using the weighted average structural decomposition and K-mean model. This paper focus on four driving factors including carbon emission intensity, final demand structure, export structure and production structure. The carbon emission intensity and final demand structure are the main factors of carbon emission reduction and the other two factors have little effect. In G20 countries, Italy was in the first category because it does best on the final demand structure factor, however Australia is in the last category because it does not make full use of four factors. Therefore, the development of energy-saving technologies and adjustment of demand structure has become an important means for countries to transform and achieve carbon neutrality.

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Section: Literature Reviewmentioning

confidence: 99%

Driving Factors of Carbon Emissions in G20 Countries: A Weighted Average Structural Decomposition Approach

Xia

2023

Preprint

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“…The maximum number of iterations of the PSO algorithm was set to 200, the total number of particles was set to 30, and the fitness function was set to be the OOB error of RF. Before optimization, the ranges of parameters n and m were set as [100, 1000] and [1,12], respectively.…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

“…The relevant parameters of the SCSO algorithm were set as follows: the maximum number of iterations was 200, the number of sand cats was 30, and the maximum sensitivity range was 2. Similarly, the ranges of parameters n and m were set as [100, 1000] and [1,12], respectively, and the fitness function was the OOB error of RF. The specific iterative process of SCSO-RF is shown in Figure 12.…”

Section: Sand Cat Swarm Optimization Algorithmmentioning

confidence: 99%

“…In the 21st century, a new stage of water conservancy construction has been entered, marked by the Xiaowan, Xiluodu, Jinping, Merowe, Yeywa, and other large-scale hydropower stations in the world [1][2][3]. With the construction of more and more large-scale water conservancy projects, more attention has begun to be paid to the safety of ultra-high dams.…”

Section: Introductionmentioning

confidence: 99%

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Deformation Prediction System of Concrete Dam Based on IVM-SCSO-RF

Zhang

Zheng

Liu

2022

Water

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Deformation prediction is an important part of concrete dam safety monitoring. In recent years, the random forest (RF) algorithm has attracted more and more attention in the field of dam safety monitoring because of its fast speed and strong generalization ability. However, the performance of RF is easily affected by many factors, such as the drift of measured value in displacement and the inappropriate setting of parameters of RF. To solve the above problems, the indicator variable model (IVM) is used to identify and eliminate the drift of measured values in this paper, and the sand cat swarm optimization (SCSO) is applied to optimize RF for the first time. On the grounds of this, a deformation prediction system of a concrete dam based on the IVM and RF algorithm optimized by SCSO is proposed. The case study shows that IVM can correct the interference of monitoring data accurately, and the maximum error rate is less than 3%; in the aspect of parameter optimization of RF, the results of the SCSO algorithm are obviously better than those of the TAE method and PSO algorithm, and the corresponding OOB error is the minimum; in terms of prediction performance, compared with TAE-RF, PSO-RF, LSTM and SVM, SCSO-RF has higher accuracy and stronger stability, and its SSE and MSE are reduced by at least 91%, MAE and RMSE are reduced by at least 71%, and R2 is very close to 1. The results of study provide a new method for the automatic online evaluation of dam safety performance.

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“…The behavior of a concrete dam is a nonlinear dynamic evolution process in which materials and structures interact under the synergistic action of multiple factors [1,2]. As a comprehensive effect quantity of the performance of the concrete dam, deformation always attracts more attention as it indicates the transformation of the structural behavior of the dam [3][4][5]. An enormous amount of deformation monitoring data were gathered for fundamental analysis and predictions of dam deformation behavior during the lifespan of a concrete dam [6].…”

Section: Introductionmentioning

confidence: 99%

A Novel Imputation Model for Missing Concrete Dam Monitoring Data

Cui

et al. 2023

Mathematics

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To ensure the safety of concrete dams, a large number of monitoring instruments are embedded in the bodies and foundations of the dams. However, monitoring data are often missing due to failure of monitoring equipment, human error and other factors that cause difficulties in diagnosis of dam safety and failure to precisely predict their deformation. In this paper, a new method for imputing missing deformation data is proposed. First, since the traditional deformation increment speed distance index of the deformation similarity index does not take into account the fact that there is little change in deformations occurring in two consecutive days, the denominator of the index tends to be equal to zero. In this paper, an improved index for solving this problem is proposed. A combined weighting method for calculating the deformation similarity comprehensive index and the k-means clustering method is then proposed and used to classify deformation monitoring points. Subsequently, a panel data model that imputes different types of missing data is established. The method proposed in this paper can impute missing concrete dam deformation data more accurately; therefore, it can effectively solve the missing deformation monitoring data problem.

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Deformation Analysis of an Ultra-High Arch Dam under Different Water Level Conditions Based on Optimized Dynamic Panel Clustering

Cited by 12 publications

References 45 publications

Driving Factors of Carbon Emissions in G20 Countries: A Weighted Average Structural Decomposition Approach

Driving Factors of Carbon Emissions in G20 Countries: A Weighted Average Structural Decomposition Approach

Deformation Prediction System of Concrete Dam Based on IVM-SCSO-RF

A Novel Imputation Model for Missing Concrete Dam Monitoring Data

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